Abstract

Detection of automatic speech recognition (ASR) errors is crucial to preventing their further propagation through statistical machine translation (SMT) in conversational spoken language translation (CSLT) systems. In this paper, we venture beyond traditional features obtained from the ASR decoder and hypothesized word sequence, and explore additional information streams provided by an error-robust CSLT system, including SMT confidence estimates and posteriors from named entity detection (NED). Another significant novelty of this work is the use of an automated word boundary detector based on acoustic-prosodic features to verify the existence of ASR-hypothesized word boundaries, which further improves ASR error detection. Offline evaluation on a test set designed to invoke ASR errors showed that at 10% false alarm rate, the proposed features provide 2.8% absolute (4.2% relative) improvement in …

Date

2013

Authors

Wei Chen, Sankaranarayanan Ananthakrishnan, Rohit Kumar, Rohit Prasad, Prem Natarajan

Conference

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Pages

7418-7422

Publisher

IEEE